Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: David Wooff, Michael Goldstein
Title: The Bayes Linear Programming Language [B/D]
Abstract: Bayes linear methodology provides a quantitative structure for expressing our beliefs and systematic methods for revising these beliefs given observational data. Particular emphasis is placed upon interpretation of and diagnostics for the specification. The approach is similar in spirit to the standard Bayes analysis, but is constructed so as to avoid much of the burden of specification and computation of the full Bayes case. This report is the first of a series describing Bayes linear methods. In this document, we introduce some of the basic machinery of the theory. Examples, computational issues, detailed derivations of results and approaches to belief elicitation will be addressed in related reports.

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Paper: The Bayes Linear Programming Language [B/D]     Download PDF (Downloads: 6821)
blm2.pdf: Bayes Linear Methods II: An example with an introduction to [B/D] Download (Downloads: 2055; 185KB)
blm3.pdf: Bayes Linear Methods III: Analysing Bayes linear influence diagrams and Exchangeability in [B/D] Download (Downloads: 1833; 158KB)
man.pdf: [B/D] Reference Manual - Version 8.44 Download (Downloads: 1579; 1MB)

DOI: 10.18637/jss.v005.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.